{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'label'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-34131c728536>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'X-axis'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Y-axis'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[1;31m#plt.show()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'label'"
     ]
     "data": {
      "image/png": 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J8uqr6a5KRLKZAkeOUd+GVNFoh4ikkr5uckzVfBsPPaT5NiSg0Q4RSQUFjhyi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x202bc619c50>"
      ]
     },
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [3,4,5]\n",
    "y = [6,7,8]\n",
    "x1 = [1,2,3]\n",
    "y1 = [10,14,12]\n",
    "\n",
    "plt.plot(x1,y1, label = 'first line')\n",
    "plt.plot(x,y, label = 'second line')\n",
    "plt.xlabel('X-axis\\n test')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.title('Test\\n Check it out')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar charts and Histrograns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QJEna+gw2RMSWF5EkSVujfoeIiHjbUBYiSZJqy0BGIn4VEVO7tY1NKf2mkgVJ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\nIiRJUi6GCEmSlIshQpIk5WKIkCRJufx/T/sbXWvBeC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27878d117b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [2,4,6,8,10]\n",
    "y = [6,7,8,2,4]\n",
    "x1 = [1,3,4,5,6]\n",
    "y1 = [6,7,8,2,4]\n",
    "\n",
    "plt.bar(x,y, label = 'Bar1', color='b' )\n",
    "plt.bar(x1,y1, label = 'Bar2', color='r')\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.title('Interesting Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2787a187898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "population_ages = [10,22,25,30,46,55,70,77,82,66,62,73,90, 100]\n",
    "bins = [0,10,20,30,40,50,60,70,80,90,100]\n",
    "\n",
    "plt.hist(population_ages, bins, rwidth=0.8, histtype='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G7ePGjWPp0qWdXZ6IiEiH0OmMTjBv3jzGjx/fpmUmTJjA3LlzO6kiERGRjqcQ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\nFCJEREQkFIUIERERCUUhQkREREL5/wFp0BiK9PlpOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2787a1163c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1,2,3,4,5,6,7,8,9]\n",
    "y = [5,2,6,1,6,8,1,2,0]\n",
    "plt.scatter(x,y, label='skitscat', color='k', marker='*', s=100)\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.title('Interesting Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stack Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OdqkopZTyaR07dmTLli3cf//97N27l3HjxtG5c2cqVarEqlWrMn3esmXLaN26NcWLF+f8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2787a292ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "days = [1,2,3,4,5]\n",
    "sleeping = [7,8,6,11,7]\n",
    "eating = [2,3,4,3,2]\n",
    "working = [5,7,8,6,2]\n",
    "playing = [8,9,5,2,4]\n",
    "\n",
    "plt.plot([], [], color='m', label='Sleeping', linewidth=5)\n",
    "plt.plot([], [], color='c', label='Eating', linewidth=5)\n",
    "plt.plot([], [], color='r', label='Working', linewidth=5)\n",
    "plt.plot([], [], color='k', label='Playing', linewidth=5)\n",
    "\n",
    "\n",
    "plt.stackplot(days, sleeping, eating, working , playing, colors =['m', 'r', 'b' , 'k'])\n",
    "\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.legend()\n",
    "plt.title('Interesting Graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7Xsd14uB4HZpSSnUzecBfgxDZB7jK62iUJgOtEpJQT2z3QPoa1qRtZvPk8zhP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f9f72e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "slices = [7,2,2,13]\n",
    "activities = ['sleeping', 'eating', 'playing', 'working']\n",
    "cols = ['m' , 'c' , 'r' , 'b']\n",
    "\n",
    "plt.pie(slices, \n",
    "        labels = activities,\n",
    "        colors = cols,\n",
    "        startangle = 90, \n",
    "        shadow = True,\n",
    "        explode=(0,0.1,0,0), \n",
    "        autopct='%1.1f%%')\n",
    "\n",
    "#plt.xlabel('X-axis')\n",
    "#plt.ylabel('Y-axis')\n",
    "#plt.legend()\n",
    "plt.title('Interesting Graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data from files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JwBnAu3MWZWbzk+N8EJuyZEk6BbZDhFn7ZV/OiIg7gLfVbmZWMWWYg2jk4UqzzihDJ8LM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\nKPCdmW0REcC3gWfmqsvMzDZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f9f9860b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "with open('example.txt', 'r' ) as csvfile:\n",
    "    plots = csv.reader(csvfile, delimiter = ',')\n",
    "    for row in plots:\n",
    "        x.append(int(row[0]))\n",
    "        y.append(int(row[1]))\n",
    "\n",
    "plt.plot(x,y, label='Loaded from file')\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "#plt.legend()\n",
    "plt.title('Interesting Graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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keCmJECmQzOrDH/+o6kOcoghaW2HJkrgjESleSiJECqC76sN++8UdVXmLovCs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\nJUkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f9f89f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loading by numpy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x,y = np.loadtxt('example.txt', delimiter= ',', unpack=True)\n",
    "\n",
    "plt.plot(x,y, label='Loaded from file')\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.legend()\n",
    "plt.title('Interesting Graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting data from the internet\n",
    "##### https://pythonprogramming.net/converting-date-stamps-matplotlib-tutorial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OASYA/fDyp4iIiJRRRXadmNk64JshhLtiawH8PIQwOfZYZ2AV8KUQwgOxn9cA54YQHood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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f9f8bfeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loading by numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import urllib\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "def bytespdate2num(fmt, encoding='utf-8'):\n",
    "    strconverter = mdates.strpdate2num(fmt)\n",
    "    def bytesconverter(b):\n",
    "        s = b.decode(encoding)\n",
    "        return strconverter(s)\n",
    "    return bytesconverter\n",
    "\n",
    "\n",
    "def graph_data(stock):\n",
    "    url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'\n",
    "    \n",
    "    source_code = urllib.request.urlopen(url).read().decode()\n",
    "    stock_data=[]\n",
    "    split_source = source_code.split('\\n')\n",
    "    for line in split_source:\n",
    "        split_line = line.split(',')\n",
    "        if len(split_line) == 6:\n",
    "            if 'values' not in line and 'labels' not in line:\n",
    "                stock_data.append(line)\n",
    "\n",
    "    date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,\n",
    "                                                          delimiter=',',\n",
    "                                                          unpack=True,\n",
    "                                                          converters={0: bytespdate2num('%Y%m%d')})\n",
    "    plt.plot_date(date, closep,'-', label='Price')\n",
    "    \n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title('Interesting Graph\\nCheck it out')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "graph_data('TSLA')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic customizations, rotating labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D         E\n",
      "2013-01-01 -0.111800 -1.620548 -0.218532 -1.514322  1.004491\n",
      "2013-01-02  1.196485 -0.760619 -1.474877 -1.632081 -0.378008\n",
      "2013-01-03 -0.233777  1.129291 -0.503460 -0.185553  0.080105\n",
      "2013-01-04 -1.809747 -1.636084  0.434972 -1.215569 -0.890154\n",
      "2013-01-05 -3.787071  1.023582  0.320474 -0.370517 -0.477577\n",
      "2013-01-06  0.673289 -1.509945  1.223508 -0.331645  1.496650\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "s = pd.Series([1,3,5, np.nan, 6,8])\n",
    "dates = pd.date_range('20130101', periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,5), index=dates, columns=list('ABCDE'))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A          B    C  D      E    F\n",
       "0  1 2016-11-20  1.0  3   test  foo\n",
       "1  1 2016-11-20  1.0  3  train  foo\n",
       "2  1 2016-11-20  1.0  3   test  foo\n",
       "3  1 2016-11-20  1.0  3  train  foo"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'A': 1,\n",
    "                    'B': pd.Timestamp('20161120'),\n",
    "                    'C': pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                    'D': np.array([3] * 4, dtype= 'int32'),\n",
    "                    'E': pd.Categorical([\"test\" , \"train\", \"test\", \"train\"]),\n",
    "                    'F' : 'foo'\n",
    "    })\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method _arith_method_FRAME.<locals>.f of    A          B    C  D      E    F\n",
       "0  1 2016-11-20  1.0  3   test  foo\n",
       "1  1 2016-11-20  1.0  3  train  foo\n",
       "2  1 2016-11-20  1.0  3   test  foo\n",
       "3  1 2016-11-20  1.0  3  train  foo>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df2.add)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A             int64\n",
      "B    datetime64[ns]\n",
      "C           float32\n",
      "D             int32\n",
      "E          category\n",
      "F            object\n",
      "dtype: object\n",
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "Name: A, dtype: int64\n",
      "<bound method NDFrame.abs of    A          B    C  D      E    F\n",
      "0  1 2016-11-20  1.0  3   test  foo\n",
      "1  1 2016-11-20  1.0  3  train  foo\n",
      "2  1 2016-11-20  1.0  3   test  foo\n",
      "3  1 2016-11-20  1.0  3  train  foo>\n",
      "<bound method DataFrame.count of    A          B    C  D      E    F\n",
      "0  1 2016-11-20  1.0  3   test  foo\n",
      "1  1 2016-11-20  1.0  3  train  foo\n",
      "2  1 2016-11-20  1.0  3   test  foo\n",
      "3  1 2016-11-20  1.0  3  train  foo>\n"
     ]
    }
   ],
   "source": [
    "print(df2.dtypes)\n",
    "print(df2.A)\n",
    "print(df2.abs) \n",
    "print(df2.count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A          B    C  D      E    F\n",
       "0  1 2016-11-20  1.0  3   test  foo\n",
       "1  1 2016-11-20  1.0  3  train  foo\n",
       "2  1 2016-11-20  1.0  3   test  foo\n",
       "3  1 2016-11-20  1.0  3  train  foo"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df2.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.207612</td>\n",
       "      <td>0.276114</td>\n",
       "      <td>-0.228793</td>\n",
       "      <td>-2.417673</td>\n",
       "      <td>0.070085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.125962</td>\n",
       "      <td>0.949714</td>\n",
       "      <td>-1.776673</td>\n",
       "      <td>-0.960130</td>\n",
       "      <td>1.832359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.949195</td>\n",
       "      <td>-1.408843</td>\n",
       "      <td>-1.291248</td>\n",
       "      <td>0.478333</td>\n",
       "      <td>1.203971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2013-01-04  1.207612  0.276114 -0.228793 -2.417673  0.070085\n",
       "2013-01-05  0.125962  0.949714 -1.776673 -0.960130  1.832359\n",
       "2013-01-06  0.949195 -1.408843 -1.291248  0.478333  1.203971"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pritn(df.tail(3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
      "               '2013-01-05', '2013-01-06'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "source": [
    "print(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')\n",
      "[[1 Timestamp('2016-11-20 00:00:00') 1.0 3 'test' 'foo']\n",
      " [1 Timestamp('2016-11-20 00:00:00') 1.0 3 'train' 'foo']\n",
      " [1 Timestamp('2016-11-20 00:00:00') 1.0 3 'test' 'foo']\n",
      " [1 Timestamp('2016-11-20 00:00:00') 1.0 3 'train' 'foo']]\n",
      "         A    C    D\n",
      "count  4.0  4.0  4.0\n",
      "mean   1.0  1.0  3.0\n",
      "std    0.0  0.0  0.0\n",
      "min    1.0  1.0  3.0\n",
      "25%    1.0  1.0  3.0\n",
      "50%    1.0  1.0  3.0\n",
      "75%    1.0  1.0  3.0\n",
      "max    1.0  1.0  3.0\n"
     ]
    }
   ],
   "source": [
    "print(df2.columns)\n",
    "print(df2.values)\n",
    "print( df2.describe())\n",
    "print(df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   E         D         C         B         A\n",
      "2013-01-01  1.004491 -1.514322 -0.218532 -1.620548 -0.111800\n",
      "2013-01-02 -0.378008 -1.632081 -1.474877 -0.760619  1.196485\n",
      "2013-01-03  0.080105 -0.185553 -0.503460  1.129291 -0.233777\n",
      "2013-01-04 -0.890154 -1.215569  0.434972 -1.636084 -1.809747\n",
      "2013-01-05 -0.477577 -0.370517  0.320474  1.023582 -3.787071\n",
      "2013-01-06  1.496650 -0.331645  1.223508 -1.509945  0.673289\n",
      "                   A         B         C         D         E\n",
      "2013-01-04 -1.809747 -1.636084  0.434972 -1.215569 -0.890154\n",
      "2013-01-01 -0.111800 -1.620548 -0.218532 -1.514322  1.004491\n",
      "2013-01-06  0.673289 -1.509945  1.223508 -0.331645  1.496650\n",
      "2013-01-02  1.196485 -0.760619 -1.474877 -1.632081 -0.378008\n",
      "2013-01-05 -3.787071  1.023582  0.320474 -0.370517 -0.477577\n",
      "2013-01-03 -0.233777  1.129291 -0.503460 -0.185553  0.080105\n"
     ]
    }
   ],
   "source": [
    "print(df.sort_index(axis=1, ascending=False))\n",
    "print(df.sort_values('B'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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